大数据 | 顶级SCI期刊专刊/国际会议信息7条
International Conference on Data Mining and Applications
5th International Conference on Data Mining and Applications (DMA 2019) provides a forum for researchers who address this issue and to present their work in a peer-reviewed forum. Authors are solicited to contribute to the conference by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in Data mining and Applications.
Information and Management
Special Issue on Big Data Analytics for Sustainability
• 大类 : 工程技术 - 2区
• 小类 : 计算机：信息系统 - 2区
Uneven economic development, mass consumerism and unreasonable natural or human resource utilization can lead to significant deleterious outcomes, including problems such as pollution, climate change, transportation disorder, social injustice, discrimination, crime, unfair competition, and resource deficiency. Sustainability is a paradigm for deliberating about the future in which environmental, societal and economic considerations are all balanced in the pursuit of our improved existence.
Data-driven analytics has extensively penetrated both academic and practical spheres. By harnessing its power in processing large volumes of information, data analytics techniques help people to discover undiscovered links and make better decisions. Predictive techniques, such as those of machine learning (including artificial intelligence and deep learning), can help to guide us in solving future as well as current problems. Econometric techniques (including difference-in-differences, instrumental variables, matching techniques) allow us to learn causal relationships and their underlying mechanisms in large scale observational data that span across periods of time. However, as yet, insufficient effort has been invested into applying these techniques to the emerging set of problems regarding sustainability.
The focus of this special issue is the application of data analytics to understand better sustainability issues, and to inform stakeholders of possible solutions. Large scale data’s potential impact on alleviating sustainability issues hinges on several aspects. First, interactions between people, resources, ecosystems and climate can be analysed using data gathered from society and the environment. Second, interactions between consumers, companies, suppliers and markets can be optimized for sustainable development. Third, the inter-related impacts that the business world and natural world have on each other also need to be further investigated.
This special issue focuses on high quality, up-to-date technologies and solutions related to data analytics for sustainability. To our knowledge, it is the first special issue of its kind focusing on these converging topics. We aim to serve as a special issue for researchers all over the world to discuss their current work and recent advances in this field. Both theoretical studies and state-of-the-art practical applications are welcomed for submission. All submitted papers will be thoroughly peer-reviewed and selected on the basis of both their quality and their relevance to the theme of this special issue. Papers invited for revision will be invited to present their research at a conference held at King’s College London in June 2019.
The list of possible topics for this special issue includes, but is not limited to:
The impacts of business operations on society and the environment using fine-grained data.
Data analytics for equality, diversity and consumer protection.
Data analytics for improving health outcomes.
Data analytics for environmentally sustainable transportation systems.
Data analytics to achieve conformity to governmental regimes.
The application of data analytics for assessing environmental risks.
Data analytics for information security and resilience.
Data analytics for the circular economy.
The juxtaposition of data analytics in government and sustainability regulation.
Data analytics for smart cities and homes.
Social networks, online sharing and data analytics.
Data analytics for economic growth, employment and income opportunities.
Data analytics for democracy, social justice/inclusion, and crime fighting.
Big data driven approaches to collect and analyze large volumes of information from emerging technologies (e.g., IoT, remote sensors, wireless sensor networks, RFIDs, mobile)
Special Issue on Advances in Multi-Sensor Fusion for Body Sensor Networks: Algorithms, Architectures, and Applications II
• 大类 : 工程技术 - 1区
• 小类 : 计算机：人工智能 - 2区
• 小类 : 计算机：理论方法 - 1区
“Advances in Multi-Sensor Fusion for Body Sensor Networks: Algorithms, Architectures, and Applications II”
The Information Fusion Journal is planning the2nd edition of thespecial issue onAdvances in Multi-Sensor Fusion for Body Sensor Networks: Algorithms, Architectures, and Applications.
Multi-sensor data fusion embraces methodologies, algorithms and technologies for combining information from multiple sources into a unified picture of the observed phenomenon. Specifically in the context of Body Sensor Networks (BSNs), the general objective of sensor fusion is the integration of information from multiple, heterogeneous, noise- and error-affected sensor data source to draw a more consistent and accurate picture of a subject’s physiological, health, emotional, and/or activity status.
About a decade ago wireless sensor network (WSN) technologies and applications led to the introduction of BSNs: a particular type of WSN applied to human health. Since their inception, BSNs promised disruptive changes in several aspects of our daily life. At technological level, a BSN comprises wireless wearable physiological sensors applied to the human body (by means of skin electrodes, elastic straps, or even using smart fabrics) to enable, at low cost, continuous and real-time non-invasive monitoring. Very diversified BSN applications were proposed during the years, including prevention, early detection, and monitoring of cardiovascular, neuro-degenerative and other chronic diseases, elderly assistance at home (fall detection, pills reminder), fitness and wellness, motor rehabilitation assistance, physical activity and gestures detection, emotion recognition, and so on. On of the main key benefits of this technology is the possibility to continuously monitor vital and physiological signs without obstructing user/patient comfort in performing his/her daily activities. Indeed, in the last few years, its diffusion increased enormously with the introduction at mass industrial level of smart wearable devices (particularly smart watches and bracelets) that are able to capture several parameters such as body accelerations, electrocardiogram (ECG), pulse rate, and bio-impedance.
However, since many BSN applications require sophisticated signal processing techniques and algorithms, their design and implementation remain a challenging task still today. Sensed data streams are collected, processed, and transmitted remotely by means of wearable devices with limited resources in terms of energy availability, computational power, and storage capacity. In addition, BSN systems are often characterized by error-prone sensor data that significantly affect signal processing, pattern recognition, and machine learning performances. In this challenging scenario, the use of redundant or complementary data coupled with multi-sensor sensor data fusion methods represents an effective solution to infer high quality information from heavily corrupted or noisy signals, random and systematic error-affected sensor samples, data loss or inconsistency, and so on. Most commercially available networked wearables assume that a single device monitors a plethora of user information. In reality, BSN technology is transitioning to multi-device synchronous measurement environments. With the wearable network becoming more complex, fusion of the data from multiple, potentially heterogeneous, sensor sources become non-trivial tasks that directly impact performance of the activity monitoring application. In particular, we note that the complex processing chain used in BSN designs introduces various levels of data fusion with different levels of complexity and effectiveness. Only in recent years researchers have started developing technical solutions for effective fusion of BSN data.
This special issue aims to provide a forum for academic and industrial communities to report recent theoretical and application results related toAdvances in Multi-Sensor Fusion for Body Sensor Networksfrom the perspectives of algorithms, architectures, and applications.
Manuscripts (which should be original and not previously published either in full or in part or presented even in a more or less similar form at any other forum) covering unpublished researchthat clearly delineate the role of information fusionin the context of body sensor networks are invited.
The manuscript will be judged solely on the basis of new contributions excluding the contributions made in earlier publications. Contributions should be described in sufficient detail to be reproducible on the basis of the material presented in the paper and the references cited therein.
Topics appropriate for this special issue include (but are not necessarily limited to):
Data-level algorithms for multi-sensor fusion in BSNs (e.g. Digital Signal Processing, Coordinate Transforms, Kalman Filtering, Independent Component Analysis)
Feature-level algorithms for multi-sensor fusion in BSNs (e.g. Decision Trees, k-Nearest Neighbor, Naive-Bayes networks, Support Vector Machines)
Decision-level algorithms for multi-sensor fusion in BSNs (e.g. Dempster-Shafer theory, Boosting, bagging, plurality and reputation-based voting, stacking, multi-sensor ensemble)
Deep learning algorithms for better understanding of BSN-collected physiological signals for medical monitoring
Multi-level algorithms for multi-sensor fusion in BSNs
Hardware/software architectures (autonomic, agent-oriented, etc) for collaborative multi-sensor fusion in BSNs
Multi-sensor fusion applications in BSNs for human activity recognition
Multi-sensor fusion applications in BSNs for sport monitoring
Multi-sensor fusion applications in BSNs for emotion recognition
Multi-sensor fusion applications in BSNs for health care monitoring
International Conference on Advances in Information Mining and Management
会议地点: Nice, France
The amount of information and its complexity makes it difficult for our society to take advantage of the distributed knowledge value. Knowledge, text, speech, picture, data, opinion, and other forms of information representation, as well as the large spectrum of different potential sources (sensors, bio, geographic, health, etc.) led to the development of special mining techniques, mechanisms support, applications and enabling tools. However, the variety of information semantics, the dynamic of information update and the rapid change in user needs are challenging aspects when gathering and analyzing information.
IMMM 2019 continues a series of academic and industrial events focusing on advances in all aspects related to information mining, management, and use.
IEEE International Conference on Cloud and Big Data Computing
会议地点: Fukuoka, Japan
The IEEE International Conference on Cloud and Big Data Computing is a premier forum for researchers, practitioners and developers who are interested in cloud computing and big data to explore new ideas, techniques and tools, as well as to exchange experience. Besides the latest research achievements, the conference covers also innovative commercial data management systems, innovative commercial applications of cloud computing and big data technology, and experience in applying recent research advances to real-world problems.
IEEE CBDCom 2019 will be the fifth edition of the conference after the success of CBDCom 2015 in Beijing, CBDCom 2016 in Toulouse, CBDCom 2017 in San Francisco, and CBDCom 2018 in Guangzhou. It will continuously offer a platform for researchers to exchange novel studies, discuss important issues and explore key challenges in innovative cloud and big data for smarter world.
International Conference on Big Data Analytics And Computational Intelligence
会议地点: Chennai, India
The second International Conference on Big Data Analytics And Computational Intelligence will be held on 24-26 October 2019 at St. Joseph's College of Engineering, Chennai, Tamil Nadu India.
The growth of data both structured and unstructured will present challenges as well as opportunities for industries and academia over the next few years. With the explosive growth of data volumes, it is essential that real-time information that is of use to the business can be extracted to deliver better insights to decision-makers, understand complex patterns etc. Computational Intelligence tools offer adaptive mechanisms that enable the understanding of data in complex and changing environments. The main building blocks of computational intelligence involve computational modeling of biological and natural intelligent systems, multi-agent systems, hybrid intelligent systems etc.
The conference will provide an opportunity for the researchers to meet and discuss the latest solutions, scientific results and methods in solving intriguing problems in the fields of Big Data Analytics, Intelligent Agents and Computational Intelligence. The conference program will include workshops, special sessions and tutorials, along with prominent keynote speakers and regular paper presentations in parallel tracks. The aim of BDACI’19 is to serve as a forum to present current and future work as well as to exchange research ideas in this field.
Information Processing & Management
Methods and applications in the analysis of social data in healthcare
• 大类 : 工程技术 - 3区
• 小类 : 计算机：信息系统 - 3区
The growing availability and accessibility of diverse and relevant health-related data resources, and the rapid proliferation of technological developments in data analytics is contributing to make the most of extracting the power of these datasets, to improve diagnosis and decision making, shorten the development of new drugs from discovery to marketing approval, facilitate early outbreak detection, improve healthcare professionals training and reduce costs to name but a few examples.
Extracting the knowledge to make this a reality is still a daunting task: on the one hand, data sources are not integrated, they contain private information and are not structured. On the other hand, we still lack context- and privacy-aware algorithms to extract the knowledge after a proper curation and enrichment of the datasets.
In recent years technology has made it possible not only to get data from many healthcare settings (hospitals, primary care centers, laboratories, etc.), it also allows information to be obtained from the society itself (sensors, Internet of Things (IoT) devices, social networks, etc.). For instance, social media environments are a new source of data coming from all the community levels.
For this reason, the organization of the current special issue responds to the necessity in collecting the last efforts that have been made in these areas of research. The special issue aims to publish high-quality research papers focused on the analytics of social data related to healthcare as well as those studies and works that include the processes needed to perform such analytics.
The topics to be covered include, but are not limited to:
1. Challenges in social data analytics in healthcare:
opinion mining and sentiment analysis
privacy-aware data mining algorithms
data quality and veracity
natural language processing and text mining
trends in discovery and analysis
graph mining and community detection
2. Applications in social data analytics in healthcare:
medical skills and education
diagnosis, prognosis and prognostics